This custom nodes helps to transform latent in different ways.
You can access new features earlier by switching from the master branch to dev, but you need to remember that there may be some issues on the dev branch and some nodes' behavior may change after release.
This node can flip latent and merge original and flipped version.
Input:
latent
Fields:
direction
– can bevertically
,horizontally
orboth
multiplier
– multiply latent by specified number
Output:
latent
This node can shift latent along x and y-axis.
Input:
latent
Fields:
x_shift
– a number between -1 and 1 that indicates how much the latent should be shiftedy_shift
– a number between -1 and 1 that indicates how much the latent should be shifted
Output:
latent
Removed from version 2.0.0
This node allows to combine a lot of transforms with different parameters.
Input:
- base KSampler fields
- transform_optional – field that can take output from one of those nodes:
Mirror transform
,Shift transform
,Multiply transform
orCombine transforms
Fields: exactly matches the base KSampler
Output: exactly matches the base KSampler
Multiply, Mirror and Shift transform nodes parameters exactly match the corresponding KSampler with transforms (Latent Control)
parameters.
There are two new transform nodes:
- Latent add
- Latent interpolate
They work exactly the same as LatentAdd and LatentBlend nodes from standard node pack, but also, can multiply result by specified number.
You can apply specific offset for transform nodes.
Fields:
process_every
– a number that indicates which steps will be processedoffset
– a number that indicates offset for previous parameter. For example: ifprocess_every
is 4 andoffset
is 0, sampler apply transformation with this pattern: 0 0 0 1. This pattern will repeat again and again. Ifoffset
is 2, pattern will be 0 1 0 0, if -1 – 1 0 0 0.mode
– can beprocess_every
orskip_every
. For example, withskip_every
previous pattern (0 0 0 1) turn into this: 1 1 1 0
Output:
offset
You can combine different offsets to achieve interesting patterns. For example:
0 0 0 1 and 0 0 1 give this pattern: 0 0 1 1 0 1 0 1 1 0 0 1.
Each transform node has own one-time version. They allow to make one transform action at specified step.
Fixes some issues when sampling modified latent space.
Input:
exactly matches the VAE Decode
node
Output:
- latent
When you multiply latent by negative or big positive (bigger than 2) number and paste this latent in sampler, you can see that the image will be generated very poorly. This is because stable diffusion cannot work with such set of numbers (meaning the numbers contained in latent).
But you can prevent this behavior by sequential decode and encode latent using vae. Node Latent normalize
make this process easier.
This node also change some results even if output without this node looks good.
And it very slightly changes results from latent, which have not been modified.
Allow you to use transforms with any samplers that you like.
Inputs:
- latent
- transforms
Outputs:
- latent